The present invention, in some embodiments thereof, relates to image processing and, more particularly, to a device and method for processing computerized tomography images.
A computerized tomography (CT) system is an X-ray system used to produce cross sectional images of organs. CT systems have become a major diagnostic modality in modem medicine and are widely used for many types of exams.
During a chest and abdominal scan, for example, a CT system produces a series of cross sectional images which include lung, soft tissues, liver and bone. Radiologists are able to examine these series of cross sectional images to diagnose a disease in one or more of the scanned organs.
Most exams are performed by subjective viewing of the cross-sectional images on an electronic display. This subjective viewing makes use of subjective discrimination of tissue types by density differences.
Generally, CT images present the attenuation coefficient of the X-ray radiation. The attenuation coefficient value typically ranges from −1000 (air) to 2500 (bones). Thus, the dynamic range of a CT image spans over three orders of magnitude. As a result of this dynamic range, linear intensity window setting techniques are used to view CT images, in order to provide equated contrast with all the clinical required details within each specific imaged tissue. In a common diagnostic procedure, a single CT slice is viewed four times (lung window, soft tissues window, liver narrow window and bone window).
A known method for dealing with CT images is processing a single window (which contain all the data with a very low contrast), as a standard image with low contrast. Accordingly, this type of algorithms made effort to enhance the contrast as in a standard image format. This type of algorithm uses Histogram Equalization technique and its variations (such as: Adaptive Histogram Equalization, AHE, and Contrast Limited Adaptive Histogram Equalization, CLAHE), see, e.g., Pizer at el. 1984, Lehr at el. 1985 and Pizer at el. 1987.
Another method for the enhancement of CT images employs a Multi-scale adaptive histogram equalization (MAHE), which is a variation of Histogram Equalization technique and wavelet (Jin Y at el 2001). This method is suitable for processing only chest CT images with emphasis for diagnosis of the lung tissue disorder.
Other researches, Lerman R at el 2006, Socolinsky AD 2000, tried to deal partially with the high dynamic issue by improving the appearance of only one or two types of tissue simultaneously.
Furthers studies, Chang D C at el. 1998, Yu at el. 2004, tried to solve the problem of low contrast in CT images.
Additional method has been suggested by Socolinsky AD 2000. A new methodology is introduced for incorporating dynamic range constrain into contrast-based image fusion algorithm. This method was applied to different devices including CT.